Outliers (aka anomalies) are rare or atypical data objects that do not comply with the general behavior or model of the data. The statistics community has studied outlier detection extensively. A typical method for finding outliers includes building a clustering model of the data, and tagging all the data elements which do not belong to any cluster as outliers. Another typical technique is to fit a distribution function to the data, and tagging all those data points whose distance of the mean of the distribution function is more than a predefined number multiplied by the standard deviation as outliers. A Multidimensional Hierarchical Dataset can be represented as an OLAP Cube (aka cube, aka hypercube). Typical outlier search techniques will seek low level cells of the cube (i.e which correspond to the original elements of the dataset) as outliers.